Requirements

• Bachelor’s degree in Engineering, Computer Science, a related field, or equivalent practical experience.
• 5 years of experience in software engineering, data engineering, solution architecture, machine learning, or technical consulting, including coding experience in Python and SQL.
• Experience with data cloud ecosystem products, technologies, or languages (e.g., Apache Spark, PySpark, BigQuery, Spanner, Knowledge Catalog, AlloyDB).
• Experience designing and launching production-grade data cloud solutions, including architecting data systems on cloud platforms like GCP and building data pipelines.
• Experience training, evaluating, and deploying machine learning models in production environments.

Nice to Haves

• Experience implementing multi-agent systems using frameworks (e.g., LangGraph, CrewAI, ADK) and complex interaction patterns.
• Experience in prompt development, model evaluation, and innovative applications of AI techniques.
• Familiarity with Google Cloud data products or equivalent platforms such as Databricks, Snowflake, Palantir Foundry, and open table formats like Apache Iceberg or Delta Lake.
• Experience building applications on large language models for enterprise data, including Gemini, agents, RAG, and vector search.
• Experience migrating analytics, Spark, or database workloads across platforms with measurable cost or performance improvements.
• Knowledge of LLM-native metrics and techniques for optimizing state management and detailed tracing.

What You'll Be Doing

• Build and deliver production data and machine learning workloads with customers using PySpark, SQL, and Python on Google Cloud services like BigQuery, Managed Spark, Spanner, and AlloyDB.
• Create working proofs-of-concept using customer data, including secure de-identification of sensitive information.
• Develop data foundations for agentic solutions, such as lakehouse architectures, metadata and semantic context, and access controls to enable custom agent actions.
• Train, evaluate, and deploy machine learning and agentic components to improve customer business outcomes, with appropriate evaluation and monitoring.
• Refactor and migrate high-cost analytics, Spark, and database workloads from other platforms to Google Cloud with demonstrated cost and performance benefits.

Perks and Benefits

• Competitive salary and bonus structure.
• Equity grants through Alphabet Inc.
• Comprehensive benefits package.
• Opportunity to work directly with cutting-edge AI technologies, including access to DeepMind research and engineering teams.
• Collaborative culture with global impact, working to lead the AI revolution on Google Cloud.
• Flexible working locations including London, UK and Warsaw, Poland.